# Copyright 2022 Huawei Technologies Co., Ltd
#
# Licensed under the Apache License, Version 2.0 (the "License");
# you may not use this file except in compliance with the License.
# You may obtain a copy of the License at
#
# http://www.apache.org/licenses/LICENSE-2.0
#
# Unless required by applicable law or agreed to in writing, software
# distributed under the License is distributed on an "AS IS" BASIS,
# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
# See the License for the specific language governing permissions and
# limitations under the License.
# ============================================================================
import numpy as np
import mindspore as ms
import mindspore.nn as nn
import mindspore.ops as P


def nonlinearity(x):
    # swish
    return x * P.Sigmoid()(x)


def Normalize(in_channels, num_groups=32):
    return nn.GroupNorm(num_groups=num_groups, num_channels=in_channels, 
                        eps=1e-6, affine=True).to_float(ms.float32)


class Upsample(nn.Cell):
    def __init__(self, in_channels, with_conv, dtype=ms.float32):
        super().__init__()
        self.dtype = dtype
        self.with_conv = with_conv
        if self.with_conv:
            self.conv = nn.Conv2d(in_channels,
                                  in_channels,
                                  kernel_size=3,
                                  stride=1,
                                  pad_mode="pad",
                                  padding=1,
                                  has_bias=True).to_float(self.dtype)

    def construct(self, x):
        in_shape = x.shape[-2:]
        out_shape = tuple(2 * x for x in in_shape)
        x = P.ResizeNearestNeighbor(out_shape)(x)
        
        if self.with_conv:
            x = self.conv(x)
        return x


class Downsample(nn.Cell):
    def __init__(self, in_channels, with_conv, dtype=ms.float32):
        super().__init__()
        self.dtype = dtype
        self.with_conv = with_conv
        if self.with_conv:
            # no asymmetric padding in torch conv, must do it ourselves
            self.conv = nn.Conv2d(in_channels,
                                  in_channels,
                                  kernel_size=3,
                                  stride=2,
                                  pad_mode="valid",
                                  padding=0,
                                  has_bias=True).to_float(self.dtype)

    def construct(self, x):
        if self.with_conv:
            pad = ((0, 0), (0, 0), (0, 1), (0, 1))
            x = nn.Pad(paddings=pad)(x)
            x = self.conv(x)
        else:
            x = P.AvgPool(kernel_size=2, stride=2)(x)
        return x


class ResnetBlock(nn.Cell):
    def __init__(self, *, in_channels, out_channels=None, conv_shortcut=False,
                 dropout, temb_channels=512, dtype=ms.float32):
        super().__init__()
        self.dtype = dtype
        self.in_channels = in_channels
        out_channels = in_channels if out_channels is None else out_channels
        self.out_channels = out_channels
        self.use_conv_shortcut = conv_shortcut

        self.norm1 = Normalize(in_channels)
        self.conv1 = nn.Conv2d(in_channels,
                               out_channels,
                               kernel_size=3,
                               stride=1,
                               pad_mode="pad",
                               padding=1,
                               has_bias=True).to_float(dtype)
        if temb_channels > 0:
            self.temb_proj = nn.Dense(temb_channels,
                                      out_channels,
                                      bias_init='normal').to_float(dtype)
        self.norm2 = Normalize(out_channels)
        self.dropout = nn.Dropout(1. - dropout)
        self.conv2 = nn.Conv2d(out_channels,
                               out_channels,
                               kernel_size=3,
                               stride=1,
                               pad_mode="pad",
                               padding=1,
                               has_bias=True).to_float(dtype)
        if self.in_channels != self.out_channels:
            if self.use_conv_shortcut:
                self.conv_shortcut = nn.Conv2d(in_channels,
                                               out_channels,
                                               kernel_size=3,
                                               stride=1,
                                               pad_mode="pad",
                                               padding=1,
                                               has_bias=True).to_float(dtype)
            else:
                self.nin_shortcut = nn.Conv2d(in_channels,
                                              out_channels,
                                              kernel_size=1,
                                              stride=1,
                                              pad_mode="valid",
                                              has_bias=True).to_float(dtype)

    def construct(self, x, temb):
        h = x
        h = self.norm1(h)
        h = nonlinearity(h)
        h = self.conv1(h)

        if temb is not None:
            h = h + self.temb_proj(nonlinearity(temb))[:,:,None,None]

        h = self.norm2(h)
        h = nonlinearity(h)
        h = self.dropout(h)
        h = self.conv2(h)

        if self.in_channels != self.out_channels:
            if self.use_conv_shortcut:
                x = self.conv_shortcut(x)
            else:
                x = self.nin_shortcut(x)

        return x+h


class AttnBlock(nn.Cell):
    def __init__(self, in_channels, dtype=ms.float32):
        super().__init__()
        self.in_channels = in_channels
        self.dtype = dtype
        self.norm = Normalize(in_channels)
        self.q = nn.Conv2d(in_channels,
                           in_channels,
                           kernel_size=1,
                           stride=1,
                           pad_mode="valid",
                           has_bias=True).to_float(dtype)
        self.k = nn.Conv2d(in_channels,
                           in_channels,
                           kernel_size=1,
                           stride=1,
                           pad_mode="valid",
                           has_bias=True).to_float(dtype)
        self.v = nn.Conv2d(in_channels,
                           in_channels,
                           kernel_size=1,
                           stride=1,
                           pad_mode="valid",
                           has_bias=True).to_float(dtype)
        self.proj_out = nn.Conv2d(in_channels,
                                  in_channels,
                                  kernel_size=1,
                                  stride=1,
                                  pad_mode="valid",
                                  has_bias=True).to_float(dtype)

    def construct(self, x):
        h_ = x
        h_ = self.norm(h_)
        q = self.q(h_)
        k = self.k(h_)
        v = self.v(h_)

        # compute attention
        b,c,h,w = q.shape
        q = P.reshape(q, (b, c, h*w))
        q = P.transpose(q, (0, 2, 1)) # b,hw,c
        k = P.reshape(k, (b, c, h*w)) # b,c,hw
        w_ = P.bmm(q,k)     # b,hw,hw    w[b,i,j]=sum_c q[b,i,c]k[b,c,j]
        w_ = w_ * (int(c)**(-0.5))
        w_ = P.Softmax(axis=2)(w_)

        # attend to values
        v = P.reshape(v, (b, c, h*w))
        w_ = P.transpose(w_, (0, 2, 1))   # b,hw,hw (first hw of k, second of q)
        h_ = P.bmm(v, w_)     # b, c,hw (hw of q) h_[b,c,j] = sum_i v[b,c,i] w_[b,i,j]
        h_ = P.reshape(h_, (b, c, h, w))

        h_ = self.proj_out(h_)

        return x+h_


def make_attn(in_channels, attn_type="vanilla", dtype=ms.float32):
    assert attn_type == "vanilla", f'attn_type {attn_type} not supported'
    print(f"making attention of type '{attn_type}' with {in_channels} in_channels")
    if attn_type == "vanilla":
        return AttnBlock(in_channels, dtype=dtype)


class Encoder(nn.Cell):
    def __init__(self, *, ch, out_ch, ch_mult=(1,2,4,8), num_res_blocks,
                 attn_resolutions, dropout=0.0, resamp_with_conv=True, in_channels,
                 resolution, z_channels, double_z=True, use_linear_attn=False, 
                 attn_type="vanilla", dtype=ms.float32, **ignore_kwargs):
        super().__init__()
        # if use_linear_attn: attn_type = "linear"
        self.ch = ch
        self.temb_ch = 0
        self.num_resolutions = len(ch_mult)
        self.num_res_blocks = num_res_blocks
        self.resolution = resolution
        self.in_channels = in_channels
        self.dtype = dtype

        # downsampling
        self.conv_in = nn.Conv2d(in_channels,
                                 self.ch,
                                 kernel_size=3,
                                 stride=1,
                                 pad_mode="pad",
                                 padding=1,
                                 has_bias=True).to_float(self.dtype)

        curr_res = resolution
        in_ch_mult = (1,)+tuple(ch_mult)
        self.in_ch_mult = in_ch_mult
        self.down = nn.CellList(auto_prefix=False)
        for i_level in range(self.num_resolutions):
            block = nn.CellList()
            attn = nn.CellList()
            block_in = ch*in_ch_mult[i_level]
            block_out = ch*ch_mult[i_level]
            for i_block in range(self.num_res_blocks):
                block.append(ResnetBlock(in_channels=block_in,
                                         out_channels=block_out,
                                         temb_channels=self.temb_ch,
                                         dropout=dropout,
                                         dtype=self.dtype))
                block_in = block_out
                if curr_res in attn_resolutions:
                    attn.append(make_attn(block_in, attn_type=attn_type, dtype=self.dtype))
            downsample = Downsample(block_in, resamp_with_conv, dtype=self.dtype)
            down = nn.Cell()
            down.block = block
            down.attn = attn
            # if i_level != self.num_resolutions-1:
            #     down.downsample = downsample
            down.downsample = downsample
            curr_res = curr_res // 2
            down.update_parameters_name(prefix=self.param_prefix + f"down.{i_level}.")
            self.down.append(down)

        # middle
        self.mid = nn.Cell()
        self.mid.block_1 = ResnetBlock(in_channels=block_in,
                                       out_channels=block_in,
                                       temb_channels=self.temb_ch,
                                       dropout=dropout,
                                       dtype=self.dtype)
        self.mid.attn_1 = make_attn(block_in, attn_type=attn_type, dtype=self.dtype)
        self.mid.block_2 = ResnetBlock(in_channels=block_in,
                                       out_channels=block_in,
                                       temb_channels=self.temb_ch,
                                       dropout=dropout,
                                       dtype=self.dtype)

        # end
        self.norm_out = Normalize(block_in)
        self.conv_out = nn.Conv2d(block_in,
                                  2*z_channels if double_z else z_channels,
                                  kernel_size=3,
                                  stride=1,
                                  pad_mode="pad",
                                  padding=1,
                                  has_bias=True).to_float(self.dtype)

    def construct(self, x):
        # timestep embedding
        temb = None

        # downsampling
        hs = [self.conv_in(x)]
        for i_level in range(self.num_resolutions):
            for i_block in range(self.num_res_blocks):
                h = self.down[i_level].block[i_block](hs[-1], temb)
                if len(self.down[i_level].attn) > 0:
                    h = self.down[i_level].attn[i_block](h)
                hs.append(h)
            if i_level != self.num_resolutions-1:
                hs.append(self.down[i_level].downsample(hs[-1]))

        # middle
        h = hs[-1]
        h = self.mid.block_1(h, temb)
        h = self.mid.attn_1(h)
        h = self.mid.block_2(h, temb)

        # end
        h = self.norm_out(h)
        h = nonlinearity(h)
        h = self.conv_out(h)
        return h


class Decoder(nn.Cell):
    def __init__(self, *, ch, out_ch, ch_mult=(1,2,4,8), num_res_blocks,
                 attn_resolutions, dropout=0.0, resamp_with_conv=True, in_channels,
                 resolution, z_channels, give_pre_end=False, tanh_out=False, use_linear_attn=False,
                 attn_type="vanilla", dtype=ms.float32, **ignorekwargs):
        super().__init__()
        # if use_linear_attn: attn_type = "linear"
        self.ch = ch
        self.temb_ch = 0
        self.num_resolutions = len(ch_mult)
        self.num_res_blocks = num_res_blocks
        self.resolution = resolution
        self.in_channels = in_channels
        self.give_pre_end = give_pre_end
        self.tanh_out = tanh_out
        self.dtype = dtype

        # compute in_ch_mult, block_in and curr_res at lowest res
        in_ch_mult = (1,)+tuple(ch_mult)
        block_in = ch*ch_mult[self.num_resolutions-1]
        curr_res = resolution // 2**(self.num_resolutions-1)
        self.z_shape = (1,z_channels,curr_res,curr_res)
        print("Working with z of shape {} = {} dimensions.".format(
            self.z_shape, np.prod(self.z_shape)))

        # z to block_in
        self.conv_in = nn.Conv2d(z_channels,
                                 block_in,
                                 kernel_size=3,
                                 stride=1,
                                 pad_mode="pad",
                                 padding=1,
                                 has_bias=True).to_float(self.dtype)

        # middle
        self.mid = nn.Cell()
        self.mid.block_1 = ResnetBlock(in_channels=block_in,
                                       out_channels=block_in,
                                       temb_channels=self.temb_ch,
                                       dropout=dropout,
                                       dtype=self.dtype)
        self.mid.attn_1 = make_attn(block_in, attn_type=attn_type, dtype=self.dtype)
        self.mid.block_2 = ResnetBlock(in_channels=block_in,
                                       out_channels=block_in,
                                       temb_channels=self.temb_ch,
                                       dropout=dropout,
                                       dtype=self.dtype)

        # upsampling
        self.up = nn.CellList(auto_prefix=False)
        for i_level in reversed(range(self.num_resolutions)):
            block = nn.CellList()
            attn = nn.CellList()
            block_out = ch*ch_mult[i_level]
            for i_block in range(self.num_res_blocks+1):
                block.append(ResnetBlock(in_channels=block_in,
                                         out_channels=block_out,
                                         temb_channels=self.temb_ch,
                                         dropout=dropout,
                                         dtype=self.dtype))
                block_in = block_out
                if curr_res in attn_resolutions:
                    attn.append(make_attn(block_in, attn_type=attn_type, dtype=self.dtype))
            upsample = Upsample(block_in, resamp_with_conv, dtype=self.dtype)
            up = nn.Cell()
            up.block = block
            up.attn = attn
            # if i_level != 0:
            #     up.upsample = upsample
            up.upsample = upsample
            curr_res = curr_res * 2
            up.update_parameters_name(prefix=self.param_prefix + f"up.{i_level}.")
            if len(self.up) != 0:
                self.up.insert(0, up)
            else:
                self.up.append(up)

        # end
        self.norm_out = Normalize(block_in)
        self.conv_out = nn.Conv2d(block_in,
                                  out_ch,
                                  kernel_size=3,
                                  stride=1,
                                  pad_mode="pad",
                                  padding=1,
                                  has_bias=True).to_float(self.dtype)

    def construct(self, z):
        # timestep embedding
        temb = None

        # z to block_in
        h = self.conv_in(z)

        # middle
        h = self.mid.block_1(h, temb)
        h = self.mid.attn_1(h)
        h = self.mid.block_2(h, temb)

        # upsampling
        i_level = self.num_resolutions
        while i_level > 0:
            i_level -= 1
            for i_block in range(self.num_res_blocks+1):
                h = self.up[i_level].block[i_block](h, temb)
                if len(self.up[i_level].attn) > 0:
                    h = self.up[i_level].attn[i_block](h)

            if i_level != 0:
                h = self.up[i_level].upsample(h)

        # end
        if self.give_pre_end:
            return h
        h = self.norm_out(h)
        h = nonlinearity(h)
        h = self.conv_out(h)
        if self.tanh_out:
            h = P.tanh(h)
        return h
